tech report announcement

[Barak.Pearlmutter@F.GP.CS.CMU.EDU]

	     Using A Neural Network to Learn the Dynamics
		    of the CMU Direct-Drive Arm II
				    
		   Ken Goldberg and Barak Pearlmutter
			     CMU-CS-88-160

			       ABSTRACT

    Computing the inverse dynamics of a robot arm is an active area of
    research in the control literature.  We apply a backpropagation
    network to this problem and measure its performance on the first 2
    links of the CMU Direct-Drive Arm II for a family of
    "pick-and-place" trajectories.  Trained on a random sample of
    actual trajectories, the network is shown to generalize with a
    root mean square error/standard deviation (RMSS) of 0.10.  The
    resulting weights can be interpreted in terms of the velocity and
    acceleration filters used in conventional control theory.  We also
    report preliminary results on learning a larger subset of state
    space for a simulated arm.


If you would like a copy, you may either send computer mail to
Catherine.Copetas at cs.cmu.edu or physical mail the address below.  Please
request "technical report CMU-CS-88-160."

	Catherine Copetas
	Department of Computer Science
	Carnegie Mellon University
	Pittsburgh, PA  15213